CB619

Wind trajectory

Show code
library(GeoPressureR)
library(tidyverse)
library(leaflet)
library(leaflet.extras)
library(raster)
library(dplyr)
library(ggplot2)
library(plotly)
knitr::opts_chunk$set(echo = FALSE)
load(paste0("../data/1_pressure//", params$gdl_id, "_pressure_prob.Rdata"))
load(paste0("../data/3_static/", params$gdl_id, "_static_prob.Rdata"))
# load(paste0("../data/4_basic_graph/", params$gdl_id, "_basic_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_wind_graph.Rdata"))
load(paste0("../data/5_wind_graph/", params$gdl_id, "_grl.Rdata"))
col <- rep(RColorBrewer::brewer.pal(8, "Dark2"), times = ceiling(max(pam$sta$sta_id) / 8))

Altitude

Altitudes are computed based on pressure measurement of the geolocation, corrected based on the assumed location of the shortest path. This correction accounts therefore for the natural variation of pressure as estimated by ERA-5. The vertical lines indicate the sunrise (dashed) and sunset (solid).

Show code
p <- ggplot() +
  # geom_line(data = pam$pressure, aes(x = date, y = obs), colour = "grey") +
  geom_line(data = do.call("rbind", shortest_path_timeserie), aes(x = date, y = altitude)) +
  geom_line(data = do.call("rbind", shortest_path_timeserie) %>% filter(sta_id > 0), aes(x = date, y = altitude, col = factor(sta_id))) +
  # geom_vline(data = twl, aes(xintercept = twilight, linetype = ifelse(rise, "dashed", "solid"), color="grey"), lwd=0.1) +
  theme_bw() +
  scale_colour_manual(values = col) +
  scale_y_continuous(name = "Altitude (m)")

ggplotly(p, dynamicTicks = T) %>% layout(showlegend = F)

Wintering location

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file <- paste0("figure_print/wintering_location/wintering_location_",params$gdl_id,".png")
if(file.exists(file)){
  knitr::include_graphics(file)
}

Latitude time

Show code
 tmp <- lapply(pressure_prob, function(x) {
    mt <- metadata(x)
    df <- data.frame(
      start = mt$temporal_extent[1],
      end = mt$temporal_extent[2],
      sta_id = mt$sta_id
    )
  })
  tmp2 <- do.call("rbind", tmp)

sim_lat <- as.data.frame(t(path_sim$lat)) %>%
  mutate(sta_id = path_sim$sta_id) %>%
  pivot_longer(-c(sta_id)) %>%
  left_join(tmp2,by="sta_id")

sim_lat_p <- sim_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sim_lat)

sp_lat <- as.data.frame(shortest_path) %>% left_join(tmp2,by="sta_id")

sp_lat_p <- sp_lat %>%
  filter(sta_id==max(sta_id)) %>%
  mutate(start=end) %>%
  rbind(sp_lat)

p <- ggplot() +
  geom_step(data=sim_lat_p, aes(x=start, y=value, group=name), alpha=.07) +
  geom_point(data=sp_lat_p, aes(x=start, y=lat)) +
  xlab('Date') +
  ylab('Latitude') +
  theme_light()

ggplotly(p, dynamicTicks = T)

Shortest path and simulated path

The large circles indicates the shortest path (overall most likely trajectory) estimated by the graph approach. The size is proportional to the duration of stay. The small dots and grey lines represents 10 possible trajeectories of the bird according to the model.

Click on the full-screen mode button on the top-left of the map to see more details on the map.

Show code
sta_duration <- unlist(lapply(static_prob_marginal, function(x) {
  as.numeric(difftime(metadata(x)$temporal_extent[2], metadata(x)$temporal_extent[1], units = "days"))
}))
pal <- colorFactor(col, as.factor(seq_len(length(col))))
m <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl() %>%
  addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
  addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)), weight = sta_duration^(0.3) * 10)

for (i in seq_len(nrow(path_sim$lon))) {
  m <- m %>%
    addPolylines(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = 0.5, weight = 1, color = "#808080") %>%
    addCircles(lng = path_sim$lon[i, ], lat = path_sim$lat[i, ], opacity = .7, weight = 1, color = pal(factor(shortest_path$sta_id, levels = pam$sta$sta_id)))
}
m

Marginal probability map

The marginal probability map estimate the overall probability of position at each stationary period regardless of the trajectory taken by the bird. It is the most useful quantification of the uncertainty of the position of the bird.

Show code
li_s <- list()
l <- leaflet(width = "100%") %>%
  addProviderTiles(providers$Stamen.TerrainBackground) %>%
  addFullscreenControl()
for (i_r in seq_len(length(static_prob_marginal))) {
  i_s <- metadata(static_prob_marginal[[i_r]])$sta_id
  info <- metadata(static_prob_marginal[[i_r]])$temporal_extent
  info_str <- paste0(i_s, " | ", info[1], "->", info[2])
  li_s <- append(li_s, info_str)
  l <- l %>%
    addRasterImage(static_prob_marginal[[i_r]], colors = "OrRd", opacity = 0.8, group = info_str) %>%
    addCircles(lng = shortest_path$lon[i_s], lat = shortest_path$lat[i_s], opacity = 1, color = "#000", weight = 10, group = info_str)
}
l %>%
  addLayersControl(
    overlayGroups = li_s,
    options = layersControlOptions(collapsed = FALSE)
  ) %>%
  hideGroup(tail(li_s, length(li_s) - 1))

Wind assistance

Show code
  fun_marker_color <- function(norm){
    if (norm < 20){
      "darkpurple"
    } else if (norm < 35){
      "darkblue"
    } else if (norm < 50){
      "lightblue"
    } else if (norm < 60){
      "lightgreen"
    } else if (norm < 80){
      "yellow"
    } else if (norm < 100){
      "lightred"
    } else {
      "darkred"
    }
  }
  fun_NSEW <- function(angle){
    angle <- angle  %% (pi* 2)
    angle <- angle*180/pi
    if (angle < 45/2){
      "E"
    } else if (angle < 45*3/2){
      "NE"
    } else if (angle < 45*5/2){
      "N"
    } else if (angle < 45*7/2){
      "NW"
    } else if (angle < 45*9/2){
      "W"
    } else if (angle < 45*11/2){
      "SW"
    } else if (angle < 45*13/2){
      "S"
    }else if (angle < 45*15/2){
      "SE"
    } else {
      "E"
    }
  }

  sta_duration <- unlist(lapply(static_prob_marginal,function(x){as.numeric(difftime(metadata(x)$temporal_extent[2],metadata(x)$temporal_extent[1],units="days"))}))

  m <-leaflet(width = "100%") %>%
    addProviderTiles(providers$Stamen.TerrainBackground) %>%  addFullscreenControl() %>%
    addPolylines(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#808080", weight = 3) %>%
    addCircles(lng = shortest_path$lon, lat = shortest_path$lat, opacity = 1, color = "#000", weight = sta_duration^(0.3)*10)

  for (i_s in seq_len(grl$sz[3]-1)){
    if (grl$flight_duration[i_s]>5){
      edge <- which(grl$s == shortest_path$id[i_s] & grl$t == shortest_path$id[i_s+1])

      label = paste0( i_s,': ', grl$flight[[i_s]]$start, " - ", grl$flight[[i_s]]$end, "<br>",
                      "F. dur.: ", round(grl$flight_duration[i_s]), ' h <br>',
                      "GS: ", round(abs(grl$gs[edge])), ' km/h, ',fun_NSEW(Arg(grl$gs[edge])),'<br>',
                      "WS: ", round(abs(grl$ws[edge])), ' km/h, ',fun_NSEW(Arg(grl$ws[edge])),'<br>',
                      "AS: ", round(abs(grl$as[edge])), ' km/h, ',fun_NSEW(Arg(grl$as[edge])),'<br>')

      iconArrow <- makeAwesomeIcon(icon = "arrow-up",
                                   library = "fa",
                                   iconColor = "#FFF",
                                   iconRotate = (90 - Arg(grl$ws[edge])/pi*180) %% 360,
                                   squareMarker = TRUE,
                                   markerColor = fun_marker_color(abs(grl$ws[edge])))

      m <- m %>% addAwesomeMarkers(lng = (shortest_path$lon[i_s] + shortest_path$lon[i_s+1])/2,
                                   lat = (shortest_path$lat[i_s] + shortest_path$lat[i_s+1])/2,
                                   icon = iconArrow, popup = label)
    }
  }
  m

Histogram of Speed

Show code
edge <- t(graph_path2edge(path_sim$id, grl))
nj <- ncol(edge)
nsta <- ncol(path_sim$lon)

speed_df <- data.frame(
  as = abs(grl$as[edge]),
  gs = abs(grl$gs[edge]),
  ws = abs(grl$ws[edge]),
  sta_id_s = rep(head(grl$sta_id,-1), nj),
  sta_id_t = rep(tail(grl$sta_id,-1), nj),
  flight_duration = rep(head(grl$flight_duration,-1), nj),
  dist = geosphere::distGeo(
    cbind(as.vector(t(path_sim$lon[,1:nsta-1])), as.vector(t(path_sim$lat[,1:nsta-1]))),
    cbind(as.vector(t(path_sim$lon[,2:nsta])),   as.vector(t(path_sim$lat[,2:nsta])))
  ) / 1000
) %>% mutate(
  name = paste(sta_id_s,sta_id_t, sep="-")
)

plot1 <- ggplot(speed_df, aes(reorder(name, sta_id_s), gs)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot2 <- ggplot(speed_df, aes(reorder(name, sta_id_s), ws)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot3 <- ggplot(speed_df, aes(reorder(name, sta_id_s), as)) + geom_boxplot() + theme_bw() +scale_x_discrete(name = "")
plot4 <- ggplot(speed_df, aes(reorder(name, sta_id_s), flight_duration)) + geom_point() + theme_bw() +scale_x_discrete(name = "")

subplot(ggplotly(plot1), ggplotly(plot2), ggplotly(plot3), ggplotly(plot4), nrows=4, titleY=T)

Table of transition

Show code
alt_df = do.call("rbind", shortest_path_timeserie) %>%
    arrange(date) %>%
    mutate(
      sta_id_s = cummax(sta_id),
      sta_id_t = sta_id_s+1
    ) %>%
    filter(sta_id == 0 & sta_id_s > 0 ) %>%
    group_by(sta_id_s, sta_id_t) %>%
    summarise(
      alt_min = min(altitude),
      alt_max = max(altitude),
      alt_mean = mean(altitude),
      alt_med = median(altitude),
    )

  trans_df <- speed_df  %>%
    group_by(sta_id_s,sta_id_t,flight_duration) %>%
    summarise(
      as_m = mean(as),
      as_s = sd(as),
      gs_m = mean(gs),
      gs_s = sd(gs),
      ws_m = mean(ws),
      ws_s = sd(ws),
      dist_m = mean(dist),
      dist_s = sd(dist)
    ) %>%
    left_join(alt_df)

trans_df %>% kable()
sta_id_s sta_id_t flight_duration as_m as_s gs_m gs_s ws_m ws_s dist_m dist_s alt_min alt_max alt_mean alt_med
1 2 10.5 38.76909 10.108473 51.42214 10.387195 14.64007 1.212354 539.9325 109.06555 118.737223 1615.4406 599.36905 417.28212
2 3 10.0 43.70938 16.334581 52.07330 20.429100 15.84857 1.455628 520.7330 204.29100 4.650006 367.0766 65.80954 36.26451
3 4 12.5 48.69023 14.001809 52.86161 13.003303 12.81066 1.682626 660.7701 162.54129 80.237071 731.4116 268.18523 210.09587
4 5 3.5 30.07195 19.824732 32.96758 20.744644 20.76537 2.538298 115.3865 72.60625 6.809590 293.0091 134.24320 121.46860
5 6 9.0 47.93346 9.487636 77.58196 10.236067 30.26749 2.130341 698.2376 92.12461 111.613346 1327.5579 551.00790 454.51268
6 7 4.0 37.42716 15.021796 53.55771 16.580575 34.88244 2.298344 214.2308 66.32230 104.926363 661.0021 312.99806 247.65906
7 8 9.5 38.93952 7.535767 72.83968 7.994607 44.30471 2.936747 691.9769 75.94877 439.503774 1540.7775 952.01566 819.08267
8 9 3.5 42.88812 11.355233 29.68280 14.130869 16.91903 3.031936 103.8898 49.45804 582.465561 1110.6473 784.11437 748.16575